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DISCO server

This module contains the helper server providing the APIs used by the decentralized and federated learning schemes.

Launching a server instance

To run the local server implementation of this folder run:

npm -w server start # from the root level

Alternatively, you can start a server instance via the NPM package:

npm i -g @epfml/disco-server
disco-server

HTTP API Specifications

ML Tasks

In both learning schemes, the DISCO server provides a list of trainable ML tasks to clients in the network. An ML task consists of:

  • the neural network model
  • the task parameters (such as descriptions, preprocessing, training modes)

Adding a new task server-side can be done in several ways. See TASK.md for more information.

Route Method Body Action
/tasks GET — Get the list of ML tasks (.json)
/tasks POST Valid ML task (*) Add a new ML task
/tasks/:taskID/model.json GET — Download the model architecture file (.json) for task taskID
/tasks/:taskID/:weightsFile GET — Download the model weights file (.bin) for task taskID

(*) See the task documentation for the exact requirements.

Federated Learning

The server receives model weight updates from participants (clients), but never receives any training data. For every task, it keeps track of connected clients and weight updates, and periodically aggregates and serves the most recent weight updates.

All endpoints listed below are implemented as messages on a WebSocket, mounted on the /feai/:taskID/:clientID/ route. It means the endpoints trigger their actions for task taskID as client clientID.

Message From To Body Action
clientConnected Client Server — Connect client clientID to task taskID
postWeightsToServer Client Server Model weight updates Send model weight updates
latestServerRound Both Both — Get the current training round and model weight updates

Decentralized Learning

The server receives neither weight updates nor data, but keeps a list of available tasks and participants (clients) available for each task.

All endpoints listed below are implemented as messages on a WebSocket, mounted on the /deai/:taskID/:clientID/ route. It means the endpoints trigger their actions for task taskID as client clientID.

Message From To Body Action
clientConnected Client Server — Connect client clientID to task taskID
SignalForPeer Client Server
PeerIsReady Client Server
PeerID Server Client
PeersForRound Server Client

For completeness, note that the Disco clients send the following messages to each other in a peer-to-peer fashion, i.e. without any intervention from the server.

Message Body Action
Weights Send model weight updates to the peer
Shares Secure Aggregation: Secret shares sent in first round
PartialSums Secure Aggregation: Partial sums sent in second round

## Deployment

Testing before deploying

The server is deployed inside a docker container, thus before deploying it, we can locally test the container to see if any new dependencies work (the container runs a 20.04 Ubuntu server). See docker guide if you have not used docker and or need to install it.

To test the server do the following steps:

sudo docker build -t disco-server .

This builds the docker image, and then run it:

sudo docker run -p 8080:8080 disco-server:latest

You can now start the server test suite as usual:

npm -w server test

Warning

If you are running a VPN, docker might not properly work, e.g. http://localhost:8080/ will result in page not found.

Deploying to the Cloud

Google App Engine

Google App Engine (GAE) creates an HTTPS certificate automatically, making this the easiest way to deploy the helper server in the Google Cloud Platform.

Since we need to install some required dependencies we deploy using Docker, we do this by choosing:

runtime: custom

in the app.yaml file.

Deployment files:

  • app.yaml - GAE app config file.
  • Dockerfile - Docker commands we specify.
  • .dockerignore - Files to ignore while building the image, e.g. node_modules/.

To change the GAE app configuration, you can modify the file app.yaml.

Warning

Make sure you allocate enough memory!

Note that the size of the container can be quite large (e.g 600mb), if the alloted memory is too small then there might be 503 errors when deploying that hard tricky to debug.

To deploy the app on GAE, you can run the following command, where disco-313515 is the current PROJECT-ID:

gcloud app deploy --project=deai-313515 app.yaml --version prod

❗ Important!

❗ This is very important
When deploying check that in the google cloud console -> app engine -> versions, that no new instance is created as this will increase the cloud costs.
This should not happen in principle due to the "--version dev" flag, it is however a good idea to check this the first time you run this command.

Some useful resources:

Docker

In the docker container we specify the environment and what dependencies to install. Perhaps most importantly, once this is this done, we specify:

  1. npm run build
  2. npm run start

The first line compiles the ts code into js, and the second one then runs the compiled code.